EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline

Area of Science:

• Neuroscience
• Quantum Physics
• Biomedical Engineering

Background:

• Neuro-psychiatric disorders lack reliable early diagnostic and predictive tools.
• Current diagnostic methods often fail to capture the complexity of brain function.
• There is a critical need for objective biomarkers for timely intervention.

Purpose of the Study:

• To develop and validate a novel algorithm for the accurate identification of neuropsychiatric and neurocognitive disorders.
• To assess the diagnostic accuracy of the quantum potential mean and variability score (qpmvs) using routine electroencephalogram (EEG) recordings.
• To explore the potential of qpmvs in differentiating various neurological conditions and predicting disease course.

Main Methods:

• Utilized routine EEG recordings from 230 participants (controls, major depression, schizophrenia, cognitive impairment).
• Developed a quantum potential mean and variability score (qpmvs) based on ultrametric analyses, p-adic numbers, and quantum theory.
• Analyzed EEG signals reflecting holistic brain function, connectivity, and quantum-like information processing.

Main Results:

• The qpmvs algorithm demonstrated high accuracy in distinguishing healthy controls from patients with schizophrenia, depression, Alzheimer's disease (AD), and mild cognitive impairment (MCI).
• Significant accuracy (p<0.0001) was achieved in differentiating between schizophrenia, depression, AD, and MCI.
• The algorithm showed high area under the curve values for all comparisons, indicating robust diagnostic performance.

Conclusions:

• The novel EEG analytic algorithm (qpmvs) is a promising tool for the accurate diagnosis of neuropsychiatric and neurocognitive diseases.
• qpmvs may offer a non-invasive method for early detection and differentiation of complex neurological conditions.
• This approach holds potential for predicting disease course and guiding therapeutic strategies.